Journal of Artificial Intelligence, Applications and Innovations

Cervical cancer remains a significant global health concern, ranking as the fourth most common cancer among women. Early detection through Pap smear screening is vital for improving treatment outcomes. Computer-aided detection systems can support clinical decision-making by providing accurate and timely diagnoses. This paper proposes a deep learning model for automated cervical cancer detection using Pap smear images. Pre-trained Convolutional Neural Networks (CNNs), InceptionV3, InceptionResNetV2, and MobileNetV2, are fine-tuned with additional layers to extract specialized features through transfer learning. The extracted feature vectors are concatenated to form a unified representation, which is then used as input to multiple classification algorithms. Among these, the Bagging classifier with Random Forest as the base estimator achieves the highest performance. The model attained 97.25% accuracy, 97.26% precision, 97.28% recall, and a 97.26% F1-score on the SIPaKMeD dataset. It also achieved 96.72% accuracy on the Herlev dataset and 99.47% on the Mendeley Liquid-Based Cytology dataset. The results show that the proposed approach consistently outperforms individual CNN baselines as well as several state-of-the-art methods reported in the literature.

The growing complexity and volume of digital advertising have made recommender systems essential for enhancing user engagement and campaign performance. However, existing models predominantly rely on behavioral data, neglecting critical psychological and subconscious dimensions of user perception. This study introduces a novel hybrid recommender system that integrates multidimensional inputs, personality traits (Big Five model), subconscious associations (captured via ZMET), customer inspiration scores, and ad content tags, to deliver more psychologically aligned advertising recommendations. Using a sample of 549 participants exposed to four distinct ads from a pool of 625, data were collected through NEO personality inventories, inspiration scales, ZMET-based image selection, and expert ad tagging. The model was evaluated using standard classification metrics, achieving an accuracy of 91.5% and an AUC of 0.957, substantially outperforming conventional approaches. Key personality traits, especially Openness and Extraversion, were identified as significant predictors of recommendation relevance. This research demonstrates the value of combining behavioral, psychological, and subconscious data to build more intelligent, human-aware recommender systems. The findings offer practical insights for designing personalized ad campaigns and improving marketing efficacy in digital environments.

This study was conducted with the purpose of “developing and validating a model of the convergence of artificial intelligence and content marketing in the digital transformation of businesses.” The research design followed a sequential exploratory mixed-method approach. In the qualitative phase, semi structured interviews were conducted with experts in communication, digital marketing, and artificial intelligence. The data were analyzed using the thematic network approach at three levels—components, dimensions, and core concepts. The output of this phase yielded five principal concepts: “content creation,” “use of automation tools,” “efficiency of data analysis,” “effective interaction and content distribution,” and “enhancement of decision making,” which formed the basis for developing the quantitative instrument. The content validity of the items was confirmed through expert judgment and the Content Validity Ratio (CVR) with a threshold of 0.62. In the quantitative phase, the final questionnaire was distributed online, and 489 valid responses were collected. A confirmatory factor analysis (CFA) was performed; nine items with low factor loadings, as well as the “search engine optimization” factor, were removed to improve model fit. The measurement model fit indices and chi square ratio were found to be satisfactory. The final outcome presents a coherent and reliable framework that clarifies the linkage between the technical capacities of artificial intelligence and the content strategic needs of startups, offering a practical roadmap for designing and assessing content quality, implementing automation, optimizing distribution, and strengthening data driven decision making.

This research investigates the efficiency of Artificial Intelligence (AI) techniques in forecasting cyberattack trends and contrasts their performance with traditional approaches in environments compliant with ISO/IEC 27001. Using simulation-based evaluations, various AI algorithms including Neural Networks, Random Forests, Support Vector Machines, and Bayesian Networks were tested alongside conventional threat detection methods such as signature-based detection and heuristic analysis. The results demonstrate that AI-driven methods surpass traditional ones in several critical metrics. Neural Networks achieved the highest detection accuracy of 97.0% and delivered the fastest incident response times at 1.2 seconds, outperforming traditional techniques that exhibited lower accuracy and slower response. Additionally, AI-based anomaly detection models like Isolation Forests successfully identified emerging attack patterns with superior detection rates and faster processing times. Bayesian Network models also provided enhanced risk assessments, aligning more closely with ISO/IEC 27001 compliance requirements compared to classical methods. Although AI solutions involve higher upfront costs, they offer improved cost-effectiveness and overall performance over the long term. This study highlights the significant benefits of integrating AI into cybersecurity frameworks, emphasizing its role in advancing threat detection, response efficiency, risk management, and regulatory adherence.

Reviewing scientific articles and comparing their status can identify scientific gaps and potential opportunities. This study focuses on the field of hybrid models of banking and artificial intelligence (AI). AI applications in banking have grown significantly, ranging from fraud detection and risk assessment to personalized customer services and automated trading systems. These technologies are not only enhancing operational efficiency but also transforming how financial institutions interact with their customers and manage risks. In this paper, after extracting data from the Scopus database, categorization was performed on 4,795 reputable articles over the past 14 years (2010-2023). Clusters were created using text mining techniques to assign subject labels in the interdisciplinary fields of AI and banking. The Box-Jenkins approach was then used to select a model on the data and predict and analyze trends over different periods. The results indicate the primary focus areas for applying AI in banking are: Innovation, Technologies and Digital Banking (58.89%), Commercial and Investment Banking (27.13%), Retail, Personal and Wealth Management Banking (9.49%), and International and Global Operations Banking (4.48%).

In this study, we present an enhanced U-Net-based model for effective image denoising, incorporating a hybrid attention mechanism that combines both spatial and channel attention. These dual attention blocks enable the network to dynamically focus on relevant features while suppressing noise across both dimensions, thereby improving denoising performance. To further refine the output and enhance perceptual quality, a Gaussian filter is applied as a post-processing step, resulting in smoother edges and better texture continuity. The model also leverages Batch Normalization and Dropout techniques to stabilize training and prevent overfitting. Experimental evaluations were conducted on the CIFAR-10 and DIV2K datasets using standard performance metrics. The proposed model achieved an accuracy of 82%, a loss of 0.01, a PSNR of 37 dB, and an SSIM of 0.94—outperforming several state-of-the-art denoising methods. These results confirm the model’s strong ability to preserve structural and textural image details while significantly reducing noise. The combination of convolutional deep learning, hybrid attention mechanisms, and post-processing filtering offers a powerful and scalable solution for image restoration tasks. Furthermore, it demonstrates strong potential for practical applications in real-world scenarios such as image quality enhancement and medical imaging.

Wrist fractures, particularly distal radius and ulna fractures, are among the most common injuries in pediatric populations. Early and accurate detection of these injuries is critical for preventing long-term complications, yet interpreting pediatric wrist radiographs remains a challenging task due to the subtle nature of some abnormalities. In response to this challenge, we propose a novel hybrid framework for automated medical image detection, combining the strengths of convolutional neural networks (CNNs) and Mamba-based encoders to capture both local and global feature dependencies. To address the challenges in fusing features from these two distinct architectures, we introduce the Feature Aggregation Attention Module (FAAM), which dynamically combines the feature maps for more robust representation. Additionally, we enhance the YOLOv11 framework by replacing conventional upsampling in the neck with the Dysample technique, which improves feature propagation and refinement. We evaluate our method on the GRAZPEDWRI-DX dataset, a comprehensive collection of pediatric wrist trauma X-rays, demonstrating significant improvements in fracture detection. Our approach achieves an mAP@0.5 of 69.12% and an mAP@0.95 of 48.4%, showcasing its effectiveness in both general and challenging detection scenarios.